Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-22T23:44:57.586Z Has data issue: false hasContentIssue false
  • English
  • Français

Selection on lean growth in a nucleus of Landrace pigs: an analysis using Gibbs sampling

Published online by Cambridge University Press:  02 September 2010

M. C. Rodriguez ,
M. Toro  and
L. Silió
M. C. Rodriguez
Affiliation:
Area de Mejora Genética Animal, CIT-IN1A Apartado 8111, 28080 Madrid, Spain
M. Toro
Affiliation:
Area de Mejora Genética Animal, CIT-IN1A Apartado 8111, 28080 Madrid, Spain
L. Silió
Affiliation:
Area de Mejora Genética Animal, CIT-IN1A Apartado 8111, 28080 Madrid, Spain
Get access

Abstract

Data from 4150 Landrace pigs tested during the period 1989-94 for backfat thickness and age at 100 kg in an open selection nucleus were analysed with the standard restricted maximum likelihood/best linear unbiased prediction method and with a Bayesian approach based on the marginal posterior distributions of parameters of interest achieved via Gibbs sampling. Breeding values and fixed effects were sampled from normal distributions and (co)variance components from inverted Wishart distributions. The Bayesian analysis indicated that the selection was effective for both traits. Assuming flat priors for the (co)variance components, the posterior means of the annual rates of response to selection for both traits were −0·473 days and −0·212 mm. The influence of informative priors constructed from (co)variances estimated in the French Landrace breed on inferences about genetic and common environmental parameters, genetic group effects and total and annual responses was also examined.

Keywords

genetic parameterslean growthMonte Carlo methodpigsselection responses
Type
Research Article
Information
Animal Science , Volume 63 , Issue 2 , October 1996 , pp. 243 - 253
Copyright
Copyright © British Society of Animal Science 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bidanel, J. P., Ducos, A., Gueblez, R. and Labroue, F. 1994. Genetic parameters of backfat thickness, age at 100 kg and ultimate pH in on-farm tested French Landrace and Large White pigs. Livestock Production Science 40: 291–301.Google Scholar
Box, G. E. P. and Tiao, G. C. 1992. Bayesian inference in statistical analysis. Wiley Classics Library.Google Scholar
Gelman, A. and Rubin, D. B. 1992. Inference from iterative simulation using multiple sequences. Statistical Science 4: 457–472.Google Scholar
Gianola, D. and Foulley, J. L. 1990. Variance estimation from integrated likelihoods (VEIL). Genetics Selection Evolution 22:403–417.Google Scholar
Gianola, D., Foulley, J. L. and Fernando, R. L. 1986. Prediction of breeding values when variances are not known. Genetics Selection Evolution 18:485–498.Google Scholar
Gianola, D., Im, S. and Macedo, F. W. 1989. A framework for prediction of breeding value. In Advances in statistical methods for genetic improvement of livestock (ed. Gianola, D. and Hammond, K.), pp. 210–238. Springer, Verlag, Berlin.Google Scholar
Groeneveld, E., Kovac, M. and Wang, T. 1990. PEST, a general purpose BLUP package for multivariate prediction and estimation. Proceedings of the fourth world congress on genetics applied to livestock production, Edinburgh, vol. XIII, pp. 488–491.Google Scholar
Jensen, J., Wang, C. S., Sorensen, D. and Gianola, D. 1994. Bayesian inference on variance and covariance components for traits influenced by maternal and direct genetic effects, using the Gibbs sampler. Ada Agrkulturae Scandinavica, Section A, Animal Science 44: 193–201.Google Scholar
Jourdain, C., Gueblez, R. and Le Henaff, G. 1989. Ajustement, a poids vif constant, des criteres de selection en ferme chez le Large White et le Landrace Francais. Journees de la Recherche Porcine en France 21: 399–404.Google Scholar
Meyer, K. 1991. Estimating variances and covariances for multivariate animal models by restricted maximum likelihood. Genetics Selection Evolution 23: 67–83.Google Scholar
Meyer, K. and Hill, W. G. 1992. Approximation of sampling variances and confidence intervals for maximum likelihood estimates of variance components. Journal of Animal Breeding and Genetics 109: 264–280.Google Scholar
Odell, P. L. and Feiveson, A. H. 1966. A numerical procedure to generate a sample covariance matrix. American Statistical Association Journal 61:198–203.Google Scholar
Quaas, R. L. 1988. Additive genetic model with groups and relationships. Journal of Dairy Science 71:1338–1345.Google Scholar
Rodriguez, M. C., Silió, L., Toro, M., Gómez, J. and Simón, B. 1993. Tendencias y parámetros genéticos en dos núcleos de cerdos Landrace y Large White. Investigación Agraria Productión y Sanidad Animates 8:139–153.Google Scholar
Silverman, B. W., 1986. Density estimation for statistics and data analysis. Chapman and Hall, London.Google Scholar
Sorensen, D., Wang, C. S., Jensen, J. and Gianola, D., 1994. Bayesian analysis of genetic change due to selection using Gibbs sampling. Genetics Selection Evolution 26: 333–360.Google Scholar
Statcom. 1990. Curvdat 90.1 user's manual. Mimeograph.Google Scholar
Varona, L. 1994. Aplicaciones del muestreo de Gibbs en modelos de genética cuantitativa: análisis de un caso de heterogeneidad de varianzas. Ph.D. thesis, Universidad de Zaragoza.Google Scholar
Varona, L., Moreno, C., Garcia-Cortes, L. A. and Altarriba, J. 1994. Estimación multicaracter de componentes de varianza y covarianza en vacuno lechero mediante muestreo de Gibbs. Revista Portuguesa de Zootecnia 1: 185–195.Google Scholar
Wang, C. S., Gianola, D., Sorensen, D. A., Jensen, J., Christensen, A. and Rutledge, J. J. 1994a. Response to selection for litter size in Danish Landrace pigs: a Bayesian analysis. Theoretical and Applied Genetics 88: 220–230.Google Scholar
Wang, C. S., Rutledge, J. J. and Gianola, D. 1994b. Bayesian analysis in mixed linear models via Gibbs sampling with an application to litter size in Iberian pigs. Genetics Selection Evolution 26: 91–115.Google Scholar
Westell, R. A., Quaas, R. L. and Van Vleck, L. D. 1988. Genetic groups in an animal model. Journal of Dairy Science 71:1310–1318.Google Scholar